This course will teach you to design, build, and optimize AI chatbots using transformers and RAG through hands-on projects and Streamlit UI.

Building Chatbots with Open Source LLMs_ Langchain and Streamlit.png

Dockerfile-NLP.tar.gz

GROQ_API_KEY

Docker-NLP

Docker-NLP-Debug

text_classification

table_question_answering

translation

summarization

sentence_similarity

text_generation_inference

retrieval strategies

vector-stores

langchain_chains_QA

sentiment-analysis

Streamlit

This course provides a comprehensive guide to designing, building, and optimizing chatbots using modern AI technologies. It begins with an introduction to chatbots, foundational concepts in AI, and the mechanics of transformer models, equipping you with a solid understanding of how natural language processing underpins chatbot systems.

You’ll learn to implement large language models (LLMs) for tasks such as summarization, translation, and inference alongside data preparation techniques and retrieval-augmented generation (RAG) workflows using tools like LangChain. You’ll also cover advanced concepts like prompt engineering, retrieval chains, and developing user interfaces with Streamlit.

The course concludes with an integration and evaluation phase, where you’ll deploy your chatbot systems and assess performance. A hands-on capstone project solidifies these skills, making this course ideal for developing robust, user-friendly chatbots using cutting-edge AI methodologies.

Build AI Chatbots with Open-Source LLMs, LangChain, and Streamlit

To load and parse text content for analysis

To load and analyze user comments uing HTML

To load HTML content from web pages for processing

What is the primary function of the LangChain `UnstructuredHTMLLoader` loader?

What is the primary function of the LangChain <code>UnstructuredHTMLLoader</code> loader?

To combine multiple text entries into a single document

To divide text into manageable segments for processing

What is the main purpose of using text splitters in LangChain?

What is the main purpose of using text splitters in LangChain?

When dealing with a large document that needs to be broken down into hierarchical sections for detailed analysis, which text splitter is best?

When dealing with a large document that needs to be broken down into hierarchical sections for detailed analysis, which text splitter is best?

What is one challenge faced when using retrieval stores for handling large datasets?

What is one challenge faced when using retrieval stores for handling large datasets?

Finding the least similar data points in the dataset

Calculating the average distance between all points

Identifying the closest data points to a given query point

What is the primary use of nearest neighbor search in retrieval stores?

What is the primary use of nearest neighbor search in retrieval stores?

Which technique involves splitting a database across multiple machines to improve scalability and performance?

Which technique involves splitting a database across multiple machines to improve scalability and performance?

It optimizes data storage by compressing the documents.

It indexes individual chunks but retrieves the full parent document for context.

It uses multiple vectors for enhanced accuracy.

It reorders context to improve processing speed.

What is the main advantage of using the `ParentDocument` retrieval type in LangChain?

What is the main advantage of using the <code>ParentDocument</code> retrieval type in LangChain?

Creating multiple query points from a single document to enhance retrieval relevance.

Compressing data to fit into memory constraints

What is the Multi Vector retrieval type especially useful for?

What is the Multi Vector retrieval type especially useful for?

The ability to process requests at a faster rate

The issue of data being too complex to be effectively indexed

The challenge of accurately interpreting user queries due to language nuances

What does semantic ambiguity in data retrieval refer to?

What does semantic ambiguity in data retrieval refer to?

Test your understanding of data LangChain workflows.

Introduction to Building Chatbots

Understanding Transformers

Understanding Large Language Models (LLMs)

Data Collection and Preparation

Optimizing RAG Workflows with LangChain

Prompt Engineering and Retrieval Chains

Chatbot User Interface Development with Streamlit

Chatbot Integration and Evaluation

Capstone Project

Conclusion and Future Developments

Quiz: LangChain Framework